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- Zhang, Junni L., et al.
(författare)
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A Comparison of Methods of Inference in Randomized Experiments from a Restricted Set of Allocations
- 2019
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Rerandomization is a strategy of increasing eciency as compared to complete randomization. The idea with rerandomization is that of removing allocations with imbalance in the observed covariates and then randomizing within the set of allocations with balance in these covariates. Standard asymptotic inference based on mean dierence estimator is however conservative after rerandomization. Given a Mahalanobis distancecriterion for removing imbalanced allocations, Li et al. (2018) derived the asymptotic distribution of the mean dierence estimator and suggesteda consistent estimator of its variance. This paper discusses several alternative methods of inference under rerandomization, and compare theirperformance with that of the method in Li et al. (2018) through a large Monte Carlo simulation. We conclude that some of the methods work better for small or moderate sample sized experiments than the method in Li et al. (2018).
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